不同街景影像微环境提取方法对街面财产犯罪解释的差异分析  

A Comparative Study of Different Street View Image Micro-environment Extraction Methods for Explaining Street Property Crimes

作　　者：柳林 谢华芳[1] 岳瀚 LIU Lin;XIE Huafang;YUE Han(Center of GeoInformatics for Public Security,School of Geography and Remote Sensing,Guangzhou University,Guangzhou 510006,China;Department of Geography,University of Cincinnati,Cincinnati,OH 45221,USA)

机构地区：[1]广州大学地理科学与遥感学院公共安全地理信息分析中心,广州510006 [2]辛辛那提大学地理系,辛辛那提OH45221

出　　处：《地球信息科学学报》2023年第7期1432-1447,共16页Journal of Geo-information Science

基　　金：广州市科技计划项目(202201010288);国家自然科学基金项目(42201257);测绘遥感信息工程国家重点实验室资助课题(21I02)。

摘　　要：作为城市的骨架,街道网络是人们日常活动的重要载体,其也在很大程度上影响着犯罪事件的分布。因此,街道微环境是研究街面犯罪空间格局及其影响机制的重要切入点。已有研究中基于街景影像的街道微环境度量,多采用语义分割单一技术提取各类环境要素在影像中的像素占比,但该方法不适宜提取行人等离散要素。近期有学者结合使用语义分割和目标检测2种技术来提取影像中复杂的环境要素,但2种方法提取的微环境要素对街面犯罪的解释还未有比较研究。为探究这一问题,本文将百度街景影像作为街道层级建成环境数据,采用上述2种做法分别提取并获得街道微观环境特征变量:第1种做法仅使用语义分割技术将所有要素以像素比例度量;第2种做法用语义分割技术将人行道、建筑、墙面、栅栏、树木和草地以像素比例度量,而用目标检测技术将街面行人和路灯以个数度量。在控制了土地利用混合度、集中劣势、街道长度和密度、犯罪吸引器和发生器后,构建零膨胀负二项回归模型以分析街道微环境特征与街面财产犯罪(如街面盗窃、扒窃)间的关系。模型中分别加入通过两种方式提取的街道微环境特征变量,并比较其对街面财产犯罪影响的差异。研究结果表明:(1)与采用语义分割单一技术提取的街道微环境特征相比,加入了结合使用语义分割和目标检测两种技术提取的街道微环境特征之后,模型表现更好,对街面财产犯罪的解释力提升了7%;尤其是对于街面行人而言,采用目标检测方法对街面行人进行计数,比采用语义分割方法提取行人的像素占比更能有效反映路段上的行人规模,其与街面财产犯罪的关联性更强,其回归系数从0.09提升到0.32,其回归系数绝对值排序从第十位提升至第三位;(2)基于街景影像提取的街道微环境特征能有效解释街面财产犯罪的发The street network channels people's routine activities,which in turn affects the distribution of crime incidents.Therefore,the street micro-environment is crucial to the fine-grained understanding and explanation of the spatial distribution of street crime.In the existing research on street micro-environment extraction from street view images,semantic segmentation technique is often used to calculate the pixel proportions of various elements,without identifying individual features and objects on street.Recently,some scholars have combined semantic segmentation and object detection technologies to extract complex street environment features,generating both pixel proportions and object counts.However,no research has compared the associations between street crime and the micro-environment features extracted by the two methods.In order to explore this issue,this study used the above two methods to extract street micro-environmental features from Baidu Street View images.The first method used semantic segmentation technique to extract pixel proportions of all features.The second method combined a semantic segmentation and an object detection technology to extract the pixel proportions or counts of individual features,e.g.,sidewalk,building,wall,fence,tree,and grass were measured as pixel proportions,and people and light posts on street were measured as counts.After controlling for land use mixture,concentrated disadvantage,street density,length of street segment,and facilities that attract or generate crime,zero-inflated negative binomial regression models were constructed to assess the impacts of the street micro-environmental features on street property crime,such as street theft and pickpocketing.The above two street micro-environment measurements were added to the models separately,and their influences on street property crime were then compared.The results show that:(1)compared with the conventional semantic segmentation method,adding the street micro-environment features extracted by combining semantic segmenta

关 键 词：街面财产犯罪 街道微环境 街景影像 街面盗窃 扒窃 零膨胀负二项回归 语义分割 目标检测 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] D924.35[自动化与计算机技术—计算机科学与技术]